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Writing Efficient Code with pandas

Intermediate

4.4+

Updated 12/2024

Learn efficient techniques in pandas to optimize your Python code.

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PythonSoftware Development4 hours14 videos45 exercises3,500 XP20,230Statement of Accomplishment

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Course Description

The ability to efficiently work with big datasets and extract valuable information is an indispensable tool for every aspiring data scientist. When working with a small amount of data, we often don’t realize how slow code execution can be. This course will build on your knowledge of Python and the pandas library and introduce you to efficient built-in pandas functions to perform tasks faster. Pandas’ built-in functions allow you to tackle the simplest tasks, like targeting specific entries and features from the data, to the most complex tasks, like applying functions on groups of entries, much faster than Python's usual methods. By the end of this course, you will be able to apply a function to data based on a feature value, iterate through big datasets rapidly, and manipulate data belonging to different groups efficiently. You will apply these methods on a variety of real-world datasets, such as poker hands or restaurant tips.

Prerequisites

Data Manipulation with pandas

1

Selecting columns and rows efficiently

The need for efficient coding I

What does time.time() measure?

Measuring time I

Measuring time II

Locate rows: .iloc[] and .loc[]

Row selection: loc[] vs iloc[]

Column selection: .iloc[] vs by name

Select random rows

Random row selection

Random column selection

2

Replacing values in a DataFrame

Replace scalar values using .replace()

Replacing scalar values I

Replace scalar values II

Replace values using lists

Replace multiple values I

Replace multiple values II

Replace values using dictionaries

Replace single values I

Replace single values II

Replace multiple values III

Most efficient method for scalar replacement

3

Efficient iterating

Looping using the .iterrows() function

Create a generator for a pandas DataFrame

The iterrows() function for looping

Looping using the .apply() function

.apply() function in every cell

.apply() for rows iteration

Vectorization over pandas series

Why vectorization in pandas is so fast?

pandas vectorization in action

Vectorization with NumPy arrays using .values()

Best method of vectorization

Vectorization methods for looping a DataFrame

4

Data manipulation using .groupby()

Data transformation using .groupby().transform

The min-max normalization using .transform()

Transforming values to probabilities

Validation of normalization

When to use transform()?

Missing value imputation using transform()

Identifying missing values

Missing value imputation

Data filtration using the filter() function

When to use filtration?

Data filtration

Congratulations!

Writing Efficient Code with pandas

Course
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Included withPremium or Teams

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*4.4

from 11 reviews

64%

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Most recent
Top reviews

Joel N.

11 months

The course was great and was fantastic in enabling one to know how to optimize their code to run faster and use resources efficiently.

Jakub Ż.

over 1 year

Yes

Kalyan B.

over 1 year

;

Octavio T.

over 1 year

Great course

Zinovii M.

over 1 year

Good to systemize knowledge

"The course was great and was fantastic in enabling one to know how to optimize their code to run faster and use resources efficiently."

Joel N.

"Yes"

Jakub Ż.

";"

Kalyan B.

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